Social Media Influencers and Artificial Intelligence: Opportunities and Challenges
Bibliographic record
Abstract
This study examines social media influencers’ (SMIs) perceptions of the role of artificial intelligence (AI) in advancing their relationships with followers, and their views of the opportunities/dangers of AI in assuring their authenticity. Adopting a qualitative phenomenological approach, the study explores how SMIs’ (a) perceive AI as a tool to help SMI activities and (b) perceive AI as a positive/negative effect on relationships with followers. Data was collected through 17 semi-structured interviews with Canadian SMIs in various fields. This study shows that AI is perceived to offer significant opportunities for SMIs including improved data analytics, enhanced connectedness, and strengthening SMIs’ relationships with their followers. However, SMIs are concerned about the dangers of AI, including loss of expressing ones’ true self and loss of honesty with their followers. Collectively, participants indicated concerns about their authenticity when AI is used. Additionally, this study reveals practical implications for SMIs by providing insights into opportunities and challenges being faced in the context of AI.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".